• KIPS Transactions on Computer and Communication Systems
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• v.7 no.9
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• pp.219-226
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• 2018

One of the systematic ways to generate the number of all cases is a combination to construct a combination tree, and its time complexity is O($2^n$). A combination tree is used for various purposes such as the graph homogeneity problem, the initial model for calculating frequent item sets, and so on. However, algorithms that must search the number of all cases of a combination are difficult to use realistically due to high time complexity. Nevertheless, as the amount of data becomes large and various studies are being carried out to utilize the data, the number of cases of searching all cases is increasing. Recently, as the GPU environment becomes popular and can be easily accessed, various attempts have been made to reduce time by parallelizing algorithms having high time complexity in a serial environment. Because the method of generating the number of all cases in combination is sequential and the size of sub-task is biased, it is not suitable for parallel implementation. The efficiency of parallel algorithms can be maximized when all threads have tasks with similar size. In this paper, we propose a method to efficiently collaborate between CPU and GPU to parallelize the problem of finding the number of all cases. In order to evaluate the performance of the proposed algorithm, we analyze the time complexity in the theoretical aspect, and compare the experimental time of the proposed algorithm with other algorithms in CPU and GPU environment. Experimental results show that the proposed CPU and GPU collaboration algorithm maintains a balance between the execution time of the CPU and GPU compared to the previous algorithms, and the execution time is improved remarkable as the number of elements increases.

• Journal of KIISE:Information Networking
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• v.36 no.4
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• pp.339-350
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• 2009

In wireless sensor networks, the limited battery resources of sensor nodes have a direct impact on network lifetime. To reduce unnecessary power consumption, it is often the case that only a minimum number of sensor nodes operate in active mode while the others are kept in sleep mode. In such a case, however, the network service can be easily unreliable if any active node is unable to perform its sensing or communication function because of an unexpected failure. Thus, for achieving reliable sensing, it is important to maintain the sensing level even when some sensor nodes fail. In this paper, we propose a new fault-tolerance scheme, called FCP(Fault-tolerant Coverage Preserving), that gives an efficient way to handle the degradation of the sensing level caused by sensor node failures. In the proposed FCP scheme, a set of backup nodes are pre-designated for each active node to be used to replace the active node in case of its failure. Experimental results show that the FCP scheme provides enhanced performance with reduced overhead in terms of sensing coverage preserving, the number of backup nodes and the amount of control messages. On the average, the percentage of coverage preserving is improved by 87.2% while the additional number of backup nodes and the additional amount of control messages are reduced by 57.6% and 99.5%, respectively, compared with previous fault-tolerance schemes.

• Korean Journal of Social Welfare Studies
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• v.40 no.3
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• pp.327-348
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• 2009

The purpose of this study is to understand the diverse lifestyles of Korean older adults by analyzing the consumption pattern of older households and its determinants. The 9th wave of the Korea Labor and Income Panel Study(KLIPS) data was used for analysis. The twenty consumption items provided by the dataset was reduced to thirteen according to the consumption purpose inherent in the item. K-means cluster analysis and multinomial logistic regression was employed to categorize the consumption pattern of older households and to analyze the determinants. The results are as follows. The consumption pattern of Korean older adults was clustered into six distinctive groups named Breadwinner, Leisure-time pursuer, Friendly outgoes, Daily-life survivor, Illness sufferer and Shelter seeker. Breadwinner, Leisure-time pursuer and Friendly outgoes were lifestyles that earn and spend more compared to the other three. Nevertheless, they differed according to the family size, indicating that the parenting burden might have direct influence on the lifestyle of Korean older adults. Older adults without parenting burden and with high level of education and economic capacity were likely to show Friendly outgoes lifestyle. On the other hand, Daily-life survivor, Illness sufferer and Shelter seeker showed lower level of spending, indicating that for those lacking in economic capacity, urgent needs such as medical need or housing need dominates the lifestyle. The results call for adequate custom policies that best fit the needs of older adults.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.463-473
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• 2019

This study aimed to investigate the relationship between soccer games of Korea vs. Japan and emotions of anti-Japan and anti-Korea, empirically. For that, this study selected 2,400 comments from Naver and 5CH where people could write their SNS comments on EAFF E-1 football championship 2017. The study results got by frequency analysis and one-way ANOVA were as follows. First, Korean showed amity with own team and hostility to the opponents, and stronger hostility toward Japan. Japanese showed hostility to own team, and it was especially strong when vs. Korea. Second, Korean showed stronger hostility toward Japan than others. Japanese showed stronger hostility to own team when vs Korea. From those results, this study could conclude that soccer games of Korea vs. Japan could be a field to express those emotions rather than effect on the emotions of anti-Korea and anti-Japan. By the empirical method of this study on the emotions of anti-Japan and anti-Korea unlike advance studies, this could receive favorable evaluation.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.99-106
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• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.

• Information Systems Review
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• v.21 no.3
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• pp.63-86
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• 2019

As the use of ICT became a part of daily social life, online community has emerged as a new type of social organization. Online community is a virtual space which enables many people to participate and contribute together to collective knowledge. Anonymity in online communities can encourage active social participation by people with various social constraints, however, anonymity can also lead to serious social pathology. As a result, it is necessary to study on what is fundamentally influencing human behavior and how people's behavior is controlled in anonymous online community. This study focuses on human identity and investigate the factors affecting human behavior control in anonymous online environment by examining various aspects of identity in online discussion community. This study empirically verifies the causal relationship between factors, including social & technical anonymities, various identity dimensions, intrinsic motivation to participate in the community, group norm conformity, and quality of discussion. It also analyzes the difference between groups by the level of anonymity, gender, age, community usage period, and discussion topic. Based on the findings, this research provides theoretical and practical implications for online community management strategies and a better culture on Internet discussion.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.253-260
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• 2006

As media and product became variety, the propensity of the people is be coming various. From diversity, there we could search for some popularity is called 'the mania.' When Mania takes shape, the product will being longer even masses in these days only have short-term life. Also there are hundreds of animations that has short-term life whom people forgot everyday they watch. However, the animations could lasting its value which has the Mania. This thesis is a studies on the constitution of Mania from animation 'Fullmetal Alchemist's Character The BONES had made. We can learn that the audience were not just like the animation, but get crazy for it by comparing Japanese animation industry in those days; before it has been shrinking and manufacturing various contents from Fullmetal Alchemist means there is enough consumtions. There are many reasons to form Mania group, but specially the character symbols at the works as a whole. From this study is to know a cause of how the animation 'Fullmetal Alchemist' made huge Mania group, and significance value of the work those group left.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.